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Agricultural nonpoint source pollution has been a major influential factor on the deterioration of water quality in the Liao River source area. Best management practices (BMPs), as a comprehensive pollution prevention system designed to reduce the impacts of agricultural activities and improve water quality, has been considered one of the most effective solutions for nonpoint source pollution control. However, economic cost has been an important element for screening the implementation of BMPs. Both pollution reduction and capital expenditure need to be resolved with the actual situation. A water quality model such as the Soil and Water Assessment Tool (SWAT) and empirical cost algorithm are important tools to assess the cost-effectiveness of the effects of BMPs on nonpoint source pollution. In this study, BMP scenarios including buffer strips (BSs), fertilizer reduction (FR), forest land increase (FLI), grassland increase (GLI), and their combination were implemented using the SWAT model; furthermore, the efficiency of their pollutants reduction and costs benefit were estimated in the watershed. The results showed that combined BMPs have better control effects than a single BMP, with "BS20 (widths 20 m) + FR15 (fertilization reduction 15%) + FLI (forest land increase)" arriving at the greatest loads reduction in the critical periods. From environmental and economic perspectives, the cost-effectiveness of interception measures is higher than that of the source control measures. The results indicated that BS was the most environmentally friendly practice, and FR was the most economically efficient out of all the BMPs. Regarding land-use changes, FLI was more environmentally friendly, and GLI was more economically efficient. The most economical and effective BMPs can be designated as follows: BS1.5 (widths 1.5 m) and FR15 (fertilization reduction 15%). Therefore, due to possible differences in government policies, it is important to consider an integrated approach for all the relevant actors and seek sustainable environmental and economic development. [ABSTRACT FROM AUTHOR]

Scarce epidemiologic research examined the associations between residential greenness and dyslipidemia or lipid levels in low/middle-income countries. Baseline statistics (2015-2017) of 39,259 rural-dwelling adults were obtained from a Chinese longitudinal study. The blood lipid level was measured utilizing an enzymatic assay method. According to the 2016 Chinese guidelines on dyslipidemia (revision), patients with dyslipidemia were defined. Participants' exposure to residential greenness was characterized by the satellite-based normalized difference vegetation index (NDVI). Mixed effects logistic regression and mixed effects linear regression were performed to assess the associations of residential greenness with dyslipidemia and lipid levels. The median (interquartile range, IQR) of 3-year average NDVI 1000-m was 0.521 (0.089) units. Each IQR increase in NDVI 1000-m was significantly linked with increased odds of hyperbetalipoproteinemia (OR = 1.33, 95%CI 1.21-1.46). The same increment in NDVI 1000-m was associated with lower total cholesterol (TC) levels and increased low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) levels. For instance, the %changes in HDL-C levels was 0.71% (95%CI 0.17%-1.26%). The above relationships were partially mediated by reducing air pollution and lowering body mass index (BMI). Interaction effect analysis observed the greenness-lipid association was stronger in males than females (i.e., NDVI 1000-m -TC association). Long-term exposure to residential greenness was associated with odds of dyslipidemia and lipid levels in Chinese rural-dwelling adults, particularly among males. Considering the cross-sectional study design, more longitudinal studies are needed to identify the causal associations., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study examined the cross-sectional and longitudinal association of sleep timing with weight status in 14- to 19-year-old adolescents in Wuhan, China. A prospective school-based study was conducted in Wuhan, China between 28 May and 29 September 2019. Data on sociodemographic information, academic performance, diet, mental health status, physical activity, sleep characteristics, body weight, and height were collected. A linear regression model and binary logistic regression model were performed. A total of 1194 adolescents were included in the analysis. Adolescents who woke up before 05:45 had higher body mass index (BMI) Z-score (odds ratio (OR) with 95% confidence interval (CI) = 1.28 (1.05, 1.57), p = 0.02) and higher odds of overweight/obesity (odds ratio (OR) with 95% confidence interval (CI) = 1.74 (1.10, 2.76), p = 0.02) at baseline after fully adjustment for covariates, compared with those who woke up after 05:45. Longitudinal data showed a nonsignificant association between waking up time and change in BMI Z-score ( p = 0.18). No association of bedtime with weight status was observed in this sample after full adjustment ( p > 0.1). Earlier waking up time might contribute to overweight and obesity in adolescents; however, more data are needed to test and elucidate this relationship.